Microbiomes in bioenergy production: From analysis to management
Introduction
Microbial resource management (MRM) [1] respectively mixed culture biotechnology (MCB) [2] is a key challenge for future bioenergy production. MRM exploits natural microbial communities, so called microbiomes (e.g. [3••], see also Box 1), for bioenergy production ranging from biogas in anaerobic digesters (AD) via electric current generation to the production of biobased chemicals and sustainable recovery of resources [2, 4, 5]. The reactor microbiome composition, structure, and activity, often involving food-webs, strongly depend on its environment. Thousands of different microbial species can contribute to a microbiome and the contributions of each cell to its functionality can, so far, not be determined.
Owing to their complexity, microbiome based bioprocesses are often operated based on bulk parameters, resulting from classical biotechnological methods covering physical, chemical, and biological parameters, such as temperature, substrate and product concentrations, turbidity, or enzyme activities [6, 7•]. When applied to natural microbial communities, these methods are representative for total system performance, and thus the entire reactor microbiome, but do not allow a segregated analysis of individual sub-functions. Recent studies, however, have demonstrated that this segregated information is needed to assess the organisms’ individual performances and thus to predict the performance of an entire microbiome — for bioenergy production, see, for example [8••, 9]. Consequently, a proactive microbiome based management of bioreactors seems only feasible, when including detailed, segregated information on the biological key component, that is, the reactor microbiome. For this purpose an adequate monitoring is indispensable, but universal protocols do not exist. Furthermore, because of the diversity of bioenergy production systems standard operating procedures (SOPs) cannot be easily established. Therefore, the establishment of reactor microbiome based routine analyses — in addition to the established biotechnological methods — is highly desired in order to enable the steering of bioenergy production processes in future. So far, many techniques used for microbiome analysis in research are very sophisticated, requiring expensive specialized equipment and specialized personnel. In addition, signal acquisition and signal interpretation are often time-consuming. These properties are contrary to the required for routine analyses, as here the perfect method would be as simple as a temperature sensor and the final results as intuitive as traffic lights.
Section snippets
Present monitoring techniques for functional microbiome diagnostics
The microbial ecology toolbox offers a wide variety of molecular and cell based analysis techniques which can be applied for microbiome diagnostics (for extensive methods review see [10, 11, 12]). Fig. 1 illustrates the different targets and levels of microbiome analysis. Classical community analyses start with extraction of molecules, for example, DNA, RNA, proteins, or phospholipid fatty acids (PLFA). DNA and RNA can be analyzed using DNA-based techniques like DGGE (denaturing gradient gel
Case studies
Obviously, the routine monitoring of reactor microbiomes has different requirements and constraints in comparison to its scientific study, especially in terms of operator knowledge, costs, and acquisition time. In both cases, however, the choice of method determines the level on which process relevant changes in the microbiome can be detected, for example, as already mentioned DNA is only a marker for presence but not for activity. Therefore, the selected method has to be adequate for assessing
Towards microbiome-based reactor management
The management of reactor microbiomes requires appropriate monitoring of complex natural communities. The case studies have shown two promising examples how functional relevant changes in the microbiome structure can be determined. Thereby we illustrated some of the basic concepts on how operator guidelines can be derived in future, but also highlighted current constraints for routine microbiome analysis. So far it is possible to reliably measure microbiome structures, including their
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgements
F.H. acknowledges support by the BMBF (Research Award “Next generation biotechnological processes — Biotechnology 2020+”) and the Helmholtz-Association (Young Investigators Group). S.M. acknowledges support by the EFRE (European Fund for Regional Development).
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